1. Field of Invention
This invention relates to incorporating a correction for dynamic variance of manipulated variables and controlled variables in steady state optimization in order to minimize dynamic violation of the controlled variables.
2. Background of the Invention
Optimization of a process having a plurality of independently controlled, manipulated variables and at least one controlled variable which is dependent on the manipulated variables using deterministic steady state process model and optimization method has widely been practiced in various industry including refining, chemical and pulp manufacturing.
Typically, results of the steady state optimization are applied to the (dynamic) process system having dynamic responses to the changes made. The resulting dynamic responses of the process alter the predicted steady state values of the controlled variables. The updated predicted steady state value of the controlled variables is subsequently used in the optimization method to calculate new change in the manipulated variables. Depending on the scope and time horizon, in practice, a steady state optimization may include feedback of actual performance into updating of the results of the steady state optimization on a continuing basis to a varying degrees; at one extreme, applications such as real-time process control, this feedback is automatic and well defined whereas at the other extreme applications such as plant wide production planning and control the feedback correction is not well defined and even not present. In case of the latter, essentially the steady state optimization is used to generate a future plan of actions but with very limited form of feedback correction mechanism for corrections to the future plan of actions when the actual performance of the process system deviates from the planned targets. In which case, the whole process of production planning and control is essentially an ad-hoc mix of a very detailed and comprehensive plan generation with almost non-existent feedback correction consistent with the initial plan generation. Instead, most often a new plan is generated with a few changes based on exception.
The constraint limits (low/high limits) of the variables used in the steady state optimization are also to be honored by the process system performance during the transient from current state to the final desired optimal steady state. These low/high limits are required to be not violated or in the least the violation be kept to the minimum.
In practice due to reasons of measured and unmeasured disturbances to the process system and due to the inherent dynamic characteristics of the process, the controlled variables would unavoidably violate their constraint limits. These dynamic constraint violations become serious and cause degrading of controllability of the process when one or more manipulated variables saturate by way of hitting one of its limits or of its final control valve output limits. Furthermore, because of marked changed in the response of the process when a manipulated variable saturates, the controllability of the process degrades significantly. Therefore, in practice, it becomes a difficult problem in which on one hand the optimizer is required to push up against the manipulated variables limit for increased optimality whereas on the other hand, at the manipulated variables limit, controllability of the process is adversely affected. Therefore, it is not uncommon to find that at times just when the process seemed to be at or very close to the optimal steady state, the process system seemed to become “out of control”. To counter this problem, in practice, most model predictive control implementation provide for what is generally known in the prior art as “linearization of valves output” for saturating manipulated variable. Although this technique offers some relief from the “out of control” problem, it practice, it introduces its own problem in that the “linearization” itself could be erroneous and therefore, induce dynamic oscillation of its own.
When a manipulated variable saturates, it essentially becomes ineffective for affecting the process in the desired manner. For instance, when a fuel gas flow valve saturates, process outlet temperature is not controllable to decreasing inlet temperature by manipulation of the fuel gas flow set point. Therefore, when this happens the process outlet temperature kind of behaves as if it is icicles hanging. That is, what is otherwise should be a two sided process response to its set point, at the manipulated variable saturation, the process response becomes one sided, either looking like icicles or spikes.
One of the objects of the invention presented herein is to maintain a certain degree of controllability of the process at or near manipulated variable valve saturation or its limit that would eliminate and minimize the loss of controllability problem mentioned above.
Therefore, there is a need to improve the process of steady state optimization that would reflect the dynamic variations of the variables. Another object of the invention presented herein is to control dynamic violation of low/high limits of the controlled variables in general.
One area where steady state optimization has widely been applied is in model predictive control, MPC. For the purpose of exposition, this application type will be used hereon for this invention, however, the method is equally applicable to other types of application involving steady state optimization as it relates to a process system having a plurality of manipulated variables and controlled variables with dynamic response characteristics. MPC has been widely applied in the process industry for control and optimization of complex multivariable processes. Intrinsically MPC attempts to optimize future behavior of complex multivariable processes while minimizing dynamic violation of low and high constraints (limits) of the variables.
In practice a number of different approaches have been developed and commercialized in dealing with dynamic constraints violation. U.S. Pat. Nos. 4,349,869 and 4,616,308 describe an implementation of MPC control called Dynamic Matrix Control incorporating a set of different tuning weights for controlled variables within the dynamic controller. These weights are adjusted as dynamic process value approaches the constraints limits. In another U.S. Pat. No. 6,122,555, and its commercial implementation known as RMPCT, it incorporates a funnel around the violating limits that permits the dynamic controller to determine a suitable trajectory returning the dynamic process value back towards the limit.
However, all of these implementations of MPC have one thing in common when it comes to constraint violation of the controlled variables. They all rely on employing in one form or another dynamic tuning weights in the dynamic controller of a model predictive controller to dampen down dynamic violation. They do not include any measure of correction in the steady state optimizer to compensate for likely dynamic violations of the constraints. The steady state optimizer is strictly formulated with reference to predicted steady state and the constraints satisfaction based on the assumption that the dynamic value of the controlled variables will eventually settle down to the predicted steady state values. Inherently in all these implementations there is this built-in unresolved problem of dynamic constraint resolution. The dynamic constraints resolution problem is essentially solved by way of feedback of the process response and the dynamic tuning weights. Therefore, most of these implementations do not offer a robust and sustained quality of control in which dynamic constraints violations are well behaved and remain within reasonable bounds. Instead, when dynamic constraints violations become excessive the control engineer is required to adjust the tuning weights albeit by trail and error method, not an easy task at all even in a moderate size MPC.
The steady state optimizer of most MPC implementations does not incorporate any explicit measure that would prevent dynamic constraint violation. The steady state optimizer is purely based on predicted steady state value of the variables and the steady state model gains.
An improved and more effective method of dealing with dynamic constraint violation is needed for robust and sustained performance of model predictive controllers.
Referring to FIG. 2, there is shown in block diagram form an implementation of as practiced in the prior art Model Predictive Control, MPC. Hereafter, as practiced in the art Model Predictive Control will be referred to as MPC. As apparent from FIG. 2, MPC block 513 is divided into a steady-state calculation and a dynamic calculation. A “plant” is also represented by block 501. The term “plant” is intended to refer to any of variety of systems, such as chemical processing facilities, oil-refining facilities.
In the interest of clarity, not all features of actual implementation of a MPC controller are described in this specification.
The dynamic MPC calculation has been studied extensively (see e.g., S. J. Qin and T. A. Badgwell, “An Overview of Industrial Model Predictive Control Technology”, in Fifth International Conference on Chemical Process Control, J. C. Kantor, C. E. Garcia, and B. Caranhan, Eds., No. 93 in AIChE Symposium Series 316, 1997, pp. 232-256). The goal of the steady state MPC calculation is to recalculate the targets for the dynamic controller 511, every time the MPC controller 513 executes, because disturbances entering the system or new input information from the operator may change the location of the optimal steady state. This separation of the MPC algorithm into a steady-state and dynamic calculation was alluded to, for example, by C. Cutler, A. Morshedi, and J. Haydel, “An Industrial Perspective on Advanced Control”, AICheE National Meeting Washington, D.C., 1983 and is now common in industrial MPC technology.
Briefly, the overall system depicted in FIG. 2, comprising as practiced in the art, MPC 513 and plant 501, operates as follows: MPC 513 performs dynamic and steady-state calculations to generate control signals reflecting optimal “inputs”, u* to plant 501. Inputs u* to plant 501 on line 502. Thus, line 502 in FIG. 2 is representative of what would be, in “real-world” application of MPC technology, a plurality of electrical control signals applied to controlled components (valves, for example) to control a plurality of controlled variables, y (pressures, flow rates, temperatures, for example) within the plant.
On the other hand, the plant 501's operation is symbolically represented in FIG. 2, by a plurality of “outputs” y which are represented in FIG. 2 as being carried on a line 503, thus line 503 is representative of a plurality of electrical signals reflecting the operational status of plant 501.
As shown in FIG. 2, output(s) y are fed back to be provided as inputs to steady-state target calculation block 512. Steady-state target calculation block 512 operates to generate so-called “target” inputs and outputs, u and y, respectively, as a function of plant output(s), y and as a function of an “objective” which is symbolically represented in FIG. 2 as being carried on a line 514. The target inputs u and outputs y are represented in FIG. 2 as being carried on line 504. Target inputs u are those inputs to plant 502 which, based on the calculations made by calculation block 513, are expected based on the MPC modeling of plant 501, to result in plant 501 operating to produce the target y.
Typically, the steady state optimizer uses a steady-state version of the dynamic process model used for the dynamic controller move calculation. The recalculated optimal steady state is then passed to the dynamic controller. The steady-state target calculation for linear MPC, represented by block 512 in FIG. 2, takes the form of a linear program (“LP”).
Although the issue of dynamic violation of the constraints in the dynamic optimization has been known to be a difficult problem, little progress has been made to develop simple and effective methods of preventing much of the dynamic violations.